Medical professionals have recently been exploiting pre-operative or pre-procedural images and intra-operative or intra-procedural images to provide a more useful and inexpensive registered image of an organ, which is the subject of a minimally invasive therapeutic intervention. For example, a tumor can be imaged both pre-operatively using a CT system and intra-operatively using an X-ray system. Digital Reconstructed Radiographs (DRRs) are reconstructed from the CT images to model the X-ray images. The pre-operative DRRs and the intra-operative images are registered and merged to provide both structural and functional information about the tumor and the effected organ. Subsequent images taken intra-operatively using the X-ray system can then be merged with the pre-operative image over time to assist the physician. The pre-operative images can provide detail about the anatomy that is the subject of the procedure. Three dimensional image modalities such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) contain high resolution information about the imaged anatomy.
The intra-operative images are typically two dimensional images that are available to provide the physician with an indication of the current state of the anatomy in question. X-ray and fluoroscopy images are typically used for these purposes. Two dimensional (2D) images take significantly less time to acquire than three dimensional (3D) images and are less intrusive to the physician. However the resolution and detail of the 2D images are inferior to that of its 3D counterpart. By combining the pre-operative and intra-operative images by registering the two images, the physician can have the benefit of the detail of the pre-operative images and the current state of the patient via the intra-operative images.
Conventional registration of a projection image to a volumetric data set involves three steps. First, computation of a simulated projection image (e.g., Digitally Reconstructed Radiographs (DRRs)) is performed given the current relative position of an X-ray source image and the volume. Second, computation of the similarity measure and/or difference measure quantifying a metric for comparing the X-ray or portal image to the DRR is performed. Third, an optimization scheme is employed which searches through the parameter space (e.g., six dimensional rigid body motion) in order to maximize the similarity measure or minimize the difference measure. Once the optimum position is found, the DRR image should match the X-ray image.
The registration of two dimensional (2D) and three dimensional (3D) images is a well-known technique. It is important to compute the DRR so that it matches the real X-ray image in terms of both brightness and contrast. In addition, a well-behaved similarity measure should be chosen that can robustly characterize a metric for the images. In order to make such an algorithm practical, the computational time has to be reduced. Based on the current state of the art, implementation of such techniques for typical 3D volume data sets have a computation time of a few minutes. Most of the computation time is spent on generating DRRs. Another factor affecting the computation time is the number of iterations that have to be computed.
One approach for reducing the computation time is to randomly sample the DRRs and only use those samples for performing computations, thereby reducing the computational complexity. However, one drawback to this approach is that the robustness of the results is compromised since less information is available to the optimizer to take an accurate step toward the global solution. For many practical applications, especially interventional scenarios, registration time is crucial. It would be desirable to be able to perform registrations in real-time or close to real-time.